Osaro, a new, San Francisco-based startup that’s developing advanced machine learning known as deep reinforcement learning, has raised $3.3 million in seed funding to take its technology to market.

The money comes from Scott Banister, Jerry Yang’s AME Cloud Ventures, and Peter Thiel, for whom Osaro cofounder Derik Pridmore once worked (first at Thiel’s hedge fund, Clarium Capital, and later as a principal at his venture firm, Founders Fund).

Why it’s interesting: Osaro’s machine intelligence software combines perception (which we’ve seen plenty of in the past, including with image identification) with decision-making abilities that will ostensibly help computer and robotic systems teach themselves to act more efficiently through trial and error (which we’ve seen more infrequently).

We talked with Pridmore yesterday to learn more about the nine-person startup. Our chat has been edited for length.

TC: You don’t have any customers yet, but one of the commercial areas you’re exploring is industrial robotics. Why should these companies pay attention to Osaro?

DP: First, industrial robots aren’t what the average person thinks they are. They don’t have brains. They aren’t flexible. They are simply very precise motors that require custom programming and then perform actions by rote.

That trend used to work, but production runs that used to take a year now take a month [in many cases], and if it takes a month to set up a robot, that’s inefficient. [Our technology] enables you to grab a robot and train it a few times and let it start training itself from there. You only have to tell it when it’s successful or it fails by giving it a score.

TC: Do you have beta customers we could name for readers?

DP: We’re working on a pilot right now. These are complex solutions, so we’re working with companies that have a critical need for our technology.

What’s the business model?

DP: Our software is like an operating system and we’ll license it.

TC: Is there a systems integration component? Who will be training the operating system?

DP: Our end goal is to make it so a low-skilled technician can set up and train one of these [robots], but that won’t happen any time soon. For now, we’ll deploy it in niche applications where there really is no other solution and we’ll build out from there.

TC: Can you learn and improve on the product based on how your customers are using it?

DP: That’s the super long term goal. If we have [the operating system] in a Foxconn factory and we’re remotely monitoring the agent on a particular robot, we can learn from that robot and make another robot in another factory smarter if we can get enough data.

TC: If you’ll forgive our asking, are you at all worried about the singularity?

DP: I have my own theories about how this will progress. As technologists, we have choices to make about whether we want technologies to make people’s lives better or something else. Obviously Google wants to help, but I worry that all of these companies’ mission statements need to be a lot more explicit about human betterment as a goal, especially when it comes to AI. For example, in the classic example of a hypothetical paper clips optimizer, if you don’t give it other values, it might endlessly produce paper clips regardless of the damage it causes. Algorithms and ultimately robotic systems can be built to be social creatures that look to humans for approval and cooperation. Philosophically, that’s what I like about Osaro’s technology: we’re programming robots to look to humans for their goals and training.